Is Instructional Design Still Mysterious?

According to an article at insidehighered.com/digital-learning/  "The field has been around for 75 years, but many still wonder what instructional designers - who are gaining acceptance in higher ed - do."  Having worked in the field for 17 years, I wonder why people (especially in higher ed) still wonder what instructional designers do.

EXCERPT:
"The practice of instructional design emerged during World War II, when the military assembled groups of psychologists and academics to create training and assessment materials for troops. In 1954, Harvard University psychology professor and author B. F. Skinner introduced the concept of programmed instructional materials through his article “The Science of Learning and the Art of Teaching.”

Within a decade, noted academics -- including Robert Gagne, widely considered the father of the field of instructional design -- had embraced the importance of assessment and learning objectives in teaching and learning.

Although higher education typically left course design up to the professors who would teach in traditional classrooms, the popularity of online courses created a need for input from professionals trained in the science of teaching, instructional methods and the technology that would make learning possible for remote students.

And now, the field is growing. A 2016 report funded by the Bill & Melinda Gates Foundation estimated that a minimum of 13,000 instructional designers work on college campuses. The U.S. Bureau of Labor Statistics last year counted 151,000 jobs -- across all school levels and industries -- for instructional designers and those with similar titles: instructional technologist and director of educational research and product strategy, for example. In 2012, CNN Money predicted the field would grow by 28.3 percent within 10 years...

“We’re going to find a digital comparison [with the face-to-face classroom], but it will further encroach on the decisions faculty believe are their domain,” [Lance Eaton, an instructional designer at Brandeis University] said. “The institution might feel otherwise, and the institutional designer will be the person in the middle trying to balance that dynamic.”
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What Is Ahead for Career and Technical Education In The Trump Administration?

The new Secretary of Education, Betsy de Vos, was viewed with trepidation by many educators. They see her as an advocate of charter schools and not a champion of K-12 public schools. In higher education, it was unclear what her focus would be because she had no experience in that area.
In her first speeches, community colleges may have felt some relief as she praised community colleges noting their importance to President Trump’s plan of expanding vocational and technical education. While community colleges do provide career and technical education, most also have a mission to provide the foundation for students to transfer to four-year colleges. The views of de Vos and the administration on that are still unclear.
Career and Technical Education (CTE) is designed to equip students with skills to prepare them for viable careers in high-growth industries. According to the association for Career and Technical education (ACTE), the top 10 hardest to fill jobs include skilled trade positions. Healthcare occupations make up 12 of the 20 fastest growing occupations. There are one million jobs open in trade, transportation and utilities sectors and more than 300,000 jobs in manufacturing.
Middle-skill jobs that require education and training beyond high school but less than a bachelor's degree make up a significant part of the economy and workforce. 
But not all of that training requires a college. Career training centers and for-profit groups have taken on many of these skill areas, and that is why college educators fear that de Vos, as with public schools, will be more in favor of that private and for-profit approach rather than colleges.
In her speeches, de Vos did not touch on issues involving transfer students, although many enroll at community colleges planning to eventually transfer to a four-year institution. The themes of her comments match the priorities talked about by the administration and Republican lawmakers (like North Carolina Representative Virginia Foxx, the chairwoman of the House education and the work force committee) which focus on facilitating vocational education, expanding the number of certificates awarded to students, and putting a greater emphasis on alternatives to the traditional model of a four-year college education.
De Vos noted that President Trump's 100-day action plan includes a call to expand vocational and technical education, and that he has called multiple paths for postsecondary education "an absolute priority" for his Administration.
Those multiple paths are unclear right now, and that uncertainty concerns many educators.

Machine Learning and AI

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What is the difference between artificial intelligence (AI) and machine learning (ML)? AI is an umbrella term to describe a branch of computer science that deals with the simulation of intelligent (human) behavior. Machine learning is a subset and currently the most common type of AI. We encounter it, consciously or unconsciously, in our every the average person will encounter.
Amber MacArthur posted in one of her recent newsletters some examples of AI & ML. Any phone "assistant" (such as Apple's iPhone assistant Siri, works because it relies on huge amounts of data, but its development is based on machine-learning technology. These machines "learn" over time based on our interactions with them. This happens without being programmed to say or do new things. 
It takes more than data for people and machines to learn. It requires being able to recognize patterns in that data and learning from them, being able to draw inferences or make predictions without being explicitly programmed to do so. It needs to do critical thinking.
Another AI example noted by MacArthur is SnapTravel. It is a chatbot that uses machine learning to run its "half-bot, half-human" service with its users. It uses SMS or Facebook via Messenger to work with a "bot" agent to book your hotel reservation.
During the 1960s and 70s, the technology alarm was that computers will be taking our jobs. It turned out that some jobs disappeared, but many more were created. The new technology alarm is that AI will take away jobs. And that will happen if people are "disinclined to technical skills" because they may not be able to earn a good living in a market economy. One prediction is that "as AI improves and gets cheaper, many of the jobs left for humans will be those so badly paid they are not worth replacing with a machine." Ouch.

 

The Exploitation of Adjunct Faculty


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Sisyphus



I recently read a very strong opinion piece by Kevin Birmingham on what he calls "The Great Shame of Our Profession" - how the humanities survive on the exploitation of adjunct faculty. Birmingham spoke at a ceremony last October for winning the Truman Capote Award for his book, The Most Dangerous Book: The Battle for James Joyce’s Ulysses . Though the audience expected a talk on literary criticism, what they got was criticism of another kind. Though he writes from the perspective of someone teaching in th humanities, this situation crosses over into all departments.

It is the Sisyphean plight of adjunct faculty trying to cobble together a "full time" academic career from a part time profession.

An excerpt:

"...I am one of over one million non-tenure-track instructors working on a temporary or contingent basis and whose position offers no possibility of tenure. To be contingent means not to know if you’ll be teaching next semester or if your class will be canceled days before it starts. Most adjuncts receive less than three weeks’ notice of an appointment. They rarely receive benefits and have virtually no say in university governance... Tenured faculty represent only 17 percent of college instructors. Part-time adjuncts are now the majority of the professoriate and its fastest-growing segment. From 1975 to 2011, the number of part-time adjuncts quadrupled. And the so-called part-time designation is misleading because most of them are piecing together teaching jobs at multiple institutions simultaneously. A 2014 congressional report suggests that 89 percent of adjuncts work at more than one institution; 13 percent work at four or more. The need for several appointments becomes obvious when we realize how little any one of them pays. In 2013, The Chronicle began collecting data on salary and benefits from adjuncts across the country... According to the 2014 congressional report, adjuncts’ median pay per course is $2,700. An annual report by the American Association of University Professors indicated that last year "the average part-time faculty member earned $16,718" from a single employer. Other studies have similar findings. Thirty-one percent of part-time faculty members live near or below the poverty line. Twenty-five percent receive public assistance, like Medicaid or food stamps. One English-department adjunct who responded to the survey said that she sold her plasma on Tuesdays and Thursdays to pay for her daughter’s day care. Another woman stated that she taught four classes a year for less than $10,000... Sixty-one percent of adjunct faculty are women."


Animating Hair Is a Lesson in STEAM

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I am a proponent of the concept of teaching in a STEAM (science, technology, engineering, art, math) framework that goes across disciplines. I have seen many attempts to use science and math in teaching art - some successful, some not.

A new project that does this in an engaging way is a collaboration between Pixar Animation Studios and Khan Academy that is sponsored by Disney. Called "Pixar in a Box," it gives a look behind-the-scenes at how artists at Pixar need to use STEM to make art.

To make balls bounce, leaves in trees move in the wind, fireworks explode or realistic rippling water takes more than drawing skills. It requires computer skills and considerations of math, science such as physics and digital humanities.



In this learning series of videos on simulations, the Pixar artists use hair as an example of an animation problem that needed to be solved. Using examples from their films, such as the character Merida in Brave with her bouncy and curly hair, you learn how millions of hairs can be simulated if you think of them as being a huge system of springs.

As the lessons progress, you can learn about animation roles and will discover what a technical director does in the animation process.

The lessons are appropriate for grades 5 and up - though I can see many adults and younger kids interested in animation from a technical or artistic side enjoying the free series.


LinkedIn's Economic Graph

I wrote earlier about LinkedIn Learning, a new effort by the company to market online training. I said then that I did not think this would displace higher education any more than MOOCs or online education. If successful, it will be disruptive and perhaps push higher education to adapt sooner.

LinkedIn’s vision is to build what it calls the Economic Graph. That graph will be created using profiles for every member of the work force, every company, and "every job and every skill required to obtain those jobs."

That concept reminded me immediately of Facebook's Social Graph. Facebook introduced the term in 2007 as a way to explain how the then new Facebook Platform would take advantage of the relationships between individuals to offer a richer online experience. The term is used in a broader sense now to refer to a social graph of all Internet users.

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LinkedIn Learning is seen as a service that connects user, skills, companies and jobs. LinkedIn acknowledges that even with about 9,000 courses on their Lynda.com platform they don't have enough content to accomplish that yet.

They are not going to turn to colleges for more content. They want to use the Economic Graph to determine the skills that they need content to provide based on corporate or local needs. That is not really a model that colleges use to develop most new courses. 

But Lynda.com content are not "courses" as we think of a course in higher ed. The training is based on short video segments and short multiple-choice quizzes. Enterprise customers can create playlists of content modules to create something course-like.

One critic of LinkedIn Learning said that this was an effort to be a "Netflix of education." That doesn't sound so bad to me. Applying data science to provide "just in time" knowledge and skills is something we have heard in education, but it has never been used in any broad or truly effective way.

The goal is to deliver the right knowledge at the right time to the right person.

One connection for higher ed is that the company says it is launching a LinkedIn Economic Graph Challenge "to encourage researchers, academics, and data-driven thinkers to propose how they would use data from LinkedIn to generate insights that may ultimately lead to new economic opportunities."

Opportunities for whom? LinkedIn or the university?

This path is similar in some ways to instances of adaptive-learning software that responds to the needs of individual students. I do like that LinkedIn Learning also is looking to "create" skills in order to fulfill perceived needs. Is there a need for training in biometric computing? Then, create training for it.

You can try https://www.linkedin.com/learning/. When I went there, it knew that I was a university professor and showed me "trending" courses such as "How to Teach with Desire2Learn," "Social Media in the Classroom" and  "How to Increase Learner Engagement." Surely, the more data I give them about my work and teaching, the more specific my recommendations will become.